{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Multi step model (vector output approach)\n",
    "\n",
    "In this notebook, we demonstrate how to:\n",
    "- prepare time series data for training a RNN forecasting model\n",
    "- get data in the required shape for the keras API\n",
    "- implement a RNN model in keras to predict the next 3 steps ahead (time *t+1* to *t+3*) in the time series. This model uses recent values of temperature and load as the model input. The model will be trained to output a vector, the elements of which are ordered predictions for future time steps.\n",
    "- enable early stopping to reduce the likelihood of model overfitting\n",
    "- evaluate the model on a test dataset\n",
    "\n",
    "The data in this example is taken from the GEFCom2014 forecasting competition<sup>1</sup>. It consists of 3 years of hourly electricity load and temperature values between 2012 and 2014. The task is to forecast future values of electricity load.\n",
    "\n",
    "<sup>1</sup>Tao Hong, Pierre Pinson, Shu Fan, Hamidreza Zareipour, Alberto Troccoli and Rob J. Hyndman, \"Probabilistic energy forecasting: Global Energy Forecasting Competition 2014 and beyond\", International Journal of Forecasting, vol.32, no.3, pp 896-913, July-September, 2016."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import warnings\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import datetime as dt\n",
    "from collections import UserDict\n",
    "from IPython.display import Image\n",
    "%matplotlib inline\n",
    "\n",
    "from common.utils import load_data, mape, TimeSeriesTensor, create_evaluation_df\n",
    "\n",
    "pd.options.display.float_format = '{:,.2f}'.format\n",
    "np.set_printoptions(precision=2)\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load data into Pandas dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>load</th>\n",
       "      <th>temp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2012-01-01 00:00:00</th>\n",
       "      <td>2,698.00</td>\n",
       "      <td>32.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-01 01:00:00</th>\n",
       "      <td>2,558.00</td>\n",
       "      <td>32.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-01 02:00:00</th>\n",
       "      <td>2,444.00</td>\n",
       "      <td>30.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-01 03:00:00</th>\n",
       "      <td>2,402.00</td>\n",
       "      <td>31.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-01 04:00:00</th>\n",
       "      <td>2,403.00</td>\n",
       "      <td>32.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        load  temp\n",
       "2012-01-01 00:00:00 2,698.00 32.00\n",
       "2012-01-01 01:00:00 2,558.00 32.67\n",
       "2012-01-01 02:00:00 2,444.00 30.00\n",
       "2012-01-01 03:00:00 2,402.00 31.00\n",
       "2012-01-01 04:00:00 2,403.00 32.00"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "energy = load_data('data/')\n",
    "energy.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "valid_start_dt = '2014-09-01 00:00:00'\n",
    "test_start_dt = '2014-11-01 00:00:00'\n",
    "\n",
    "T = 6\n",
    "HORIZON = 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "train = energy.copy()[energy.index < valid_start_dt][['load', 'temp']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler\n",
    "\n",
    "y_scaler = MinMaxScaler()\n",
    "y_scaler.fit(train[['load']])\n",
    "\n",
    "X_scaler = MinMaxScaler()\n",
    "train[['load', 'temp']] = X_scaler.fit_transform(train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Use the TimeSeriesTensor convenience class to:\n",
    "1. Shift the values of the time series to create a Pandas dataframe containing all the data for a single training example\n",
    "2. Discard any samples with missing values\n",
    "3. Transform this Pandas dataframe into a numpy array of shape (samples, time steps, features) for input into Keras\n",
    "\n",
    "The class takes the following parameters:\n",
    "\n",
    "- **dataset**: original time series\n",
    "- **H**: the forecast horizon\n",
    "- **tensor_structure**: a dictionary discribing the tensor structure in the form { 'tensor_name' : (range(max_backward_shift, max_forward_shift), [feature, feature, ...] ) }\n",
    "- **freq**: time series frequency\n",
    "- **drop_incomplete**: (Boolean) whether to drop incomplete samples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "tensor_structure = {'X':(range(-T+1, 1), ['load', 'temp'])}\n",
    "train_inputs = TimeSeriesTensor(train, 'load', HORIZON, tensor_structure)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th>tensor</th>\n",
       "      <th colspan=\"3\" halign=\"left\">target</th>\n",
       "      <th colspan=\"12\" halign=\"left\">X</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>feature</th>\n",
       "      <th colspan=\"3\" halign=\"left\">y</th>\n",
       "      <th colspan=\"6\" halign=\"left\">load</th>\n",
       "      <th colspan=\"6\" halign=\"left\">temp</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>time step</th>\n",
       "      <th>t+1</th>\n",
       "      <th>t+2</th>\n",
       "      <th>t+3</th>\n",
       "      <th>t-5</th>\n",
       "      <th>t-4</th>\n",
       "      <th>t-3</th>\n",
       "      <th>t-2</th>\n",
       "      <th>t-1</th>\n",
       "      <th>t</th>\n",
       "      <th>t-5</th>\n",
       "      <th>t-4</th>\n",
       "      <th>t-3</th>\n",
       "      <th>t-2</th>\n",
       "      <th>t-1</th>\n",
       "      <th>t</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2012-01-01 05:00:00</th>\n",
       "      <td>0.18</td>\n",
       "      <td>0.23</td>\n",
       "      <td>0.29</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0.14</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.42</td>\n",
       "      <td>0.43</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0.41</td>\n",
       "      <td>0.42</td>\n",
       "      <td>0.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-01 06:00:00</th>\n",
       "      <td>0.23</td>\n",
       "      <td>0.29</td>\n",
       "      <td>0.35</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0.14</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0.43</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0.41</td>\n",
       "      <td>0.42</td>\n",
       "      <td>0.41</td>\n",
       "      <td>0.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-01 07:00:00</th>\n",
       "      <td>0.29</td>\n",
       "      <td>0.35</td>\n",
       "      <td>0.37</td>\n",
       "      <td>0.14</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0.23</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0.41</td>\n",
       "      <td>0.42</td>\n",
       "      <td>0.41</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0.39</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "tensor              target              X                                     \\\n",
       "feature                  y           load                          temp        \n",
       "time step              t+1  t+2  t+3  t-5  t-4  t-3  t-2  t-1    t  t-5  t-4   \n",
       "2012-01-01 05:00:00   0.18 0.23 0.29 0.22 0.18 0.14 0.13 0.13 0.15 0.42 0.43   \n",
       "2012-01-01 06:00:00   0.23 0.29 0.35 0.18 0.14 0.13 0.13 0.15 0.18 0.43 0.40   \n",
       "2012-01-01 07:00:00   0.29 0.35 0.37 0.14 0.13 0.13 0.15 0.18 0.23 0.40 0.41   \n",
       "\n",
       "tensor                                   \n",
       "feature                                  \n",
       "time step            t-3  t-2  t-1    t  \n",
       "2012-01-01 05:00:00 0.40 0.41 0.42 0.41  \n",
       "2012-01-01 06:00:00 0.41 0.42 0.41 0.40  \n",
       "2012-01-01 07:00:00 0.42 0.41 0.40 0.39  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_inputs.dataframe.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[0.22, 0.42],\n",
       "        [0.18, 0.43],\n",
       "        [0.14, 0.4 ],\n",
       "        [0.13, 0.41],\n",
       "        [0.13, 0.42],\n",
       "        [0.15, 0.41]],\n",
       "\n",
       "       [[0.18, 0.43],\n",
       "        [0.14, 0.4 ],\n",
       "        [0.13, 0.41],\n",
       "        [0.13, 0.42],\n",
       "        [0.15, 0.41],\n",
       "        [0.18, 0.4 ]],\n",
       "\n",
       "       [[0.14, 0.4 ],\n",
       "        [0.13, 0.41],\n",
       "        [0.13, 0.42],\n",
       "        [0.15, 0.41],\n",
       "        [0.18, 0.4 ],\n",
       "        [0.23, 0.39]]])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_inputs['X'][:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.18, 0.23, 0.29],\n",
       "       [0.23, 0.29, 0.35],\n",
       "       [0.29, 0.35, 0.37]])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_inputs['target'][:3]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Construct validation set (keeping T hours from the training set in order to construct initial features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "look_back_dt = dt.datetime.strptime(valid_start_dt, '%Y-%m-%d %H:%M:%S') - dt.timedelta(hours=T-1)\n",
    "valid = energy.copy()[(energy.index >=look_back_dt) & (energy.index < test_start_dt)][['load', 'temp']]\n",
    "valid[['load', 'temp']] = X_scaler.transform(valid)\n",
    "valid_inputs = TimeSeriesTensor(valid, 'load', HORIZON, tensor_structure)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Implement the RNN"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will implement a RNN forecasting model with the following structure:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Image('./images/multi_step_vector_output.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "from keras.models import Model, Sequential\n",
    "from keras.layers import GRU, Dense\n",
    "from keras.callbacks import EarlyStopping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "LATENT_DIM = 5\n",
    "BATCH_SIZE = 32\n",
    "EPOCHS = 10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "model = Sequential()\n",
    "model.add(GRU(LATENT_DIM, input_shape=(T, 2)))\n",
    "model.add(Dense(HORIZON))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "model.compile(optimizer='RMSprop', loss='mse')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "gru_1 (GRU)                  (None, 5)                 120       \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 3)                 18        \n",
      "=================================================================\n",
      "Total params: 138\n",
      "Trainable params: 138\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "earlystop = EarlyStopping(monitor='val_loss', min_delta=0, patience=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 23368 samples, validate on 1461 samples\n",
      "Epoch 1/50\n",
      "23368/23368 [==============================] - 3s 110us/step - loss: 0.0220 - val_loss: 0.0069\n",
      "Epoch 2/50\n",
      "23368/23368 [==============================] - 2s 87us/step - loss: 0.0056 - val_loss: 0.0045\n",
      "Epoch 3/50\n",
      "23368/23368 [==============================] - 2s 91us/step - loss: 0.0046 - val_loss: 0.0039\n",
      "Epoch 4/50\n",
      "23368/23368 [==============================] - 2s 92us/step - loss: 0.0041 - val_loss: 0.0040\n",
      "Epoch 5/50\n",
      "23368/23368 [==============================] - 2s 88us/step - loss: 0.0039 - val_loss: 0.0034\n",
      "Epoch 6/50\n",
      "23368/23368 [==============================] - 2s 94us/step - loss: 0.0036 - val_loss: 0.0040\n",
      "Epoch 7/50\n",
      "23368/23368 [==============================] - 2s 92us/step - loss: 0.0035 - val_loss: 0.0030\n",
      "Epoch 8/50\n",
      "23368/23368 [==============================] - 2s 89us/step - loss: 0.0034 - val_loss: 0.0029\n",
      "Epoch 9/50\n",
      "23368/23368 [==============================] - 2s 94us/step - loss: 0.0033 - val_loss: 0.0028\n",
      "Epoch 10/50\n",
      "23368/23368 [==============================] - 2s 91us/step - loss: 0.0032 - val_loss: 0.0028\n",
      "Epoch 11/50\n",
      "23368/23368 [==============================] - 2s 96us/step - loss: 0.0032 - val_loss: 0.0028\n",
      "Epoch 12/50\n",
      "23368/23368 [==============================] - 2s 98us/step - loss: 0.0032 - val_loss: 0.0028\n",
      "Epoch 13/50\n",
      "23368/23368 [==============================] - 2s 91us/step - loss: 0.0031 - val_loss: 0.0029\n",
      "Epoch 14/50\n",
      "23368/23368 [==============================] - 3s 113us/step - loss: 0.0031 - val_loss: 0.0029\n",
      "Epoch 15/50\n",
      "23368/23368 [==============================] - 2s 102us/step - loss: 0.0031 - val_loss: 0.0027\n",
      "Epoch 16/50\n",
      "23368/23368 [==============================] - 3s 125us/step - loss: 0.0031 - val_loss: 0.0028\n",
      "Epoch 17/50\n",
      "23368/23368 [==============================] - 3s 111us/step - loss: 0.0031 - val_loss: 0.0027\n",
      "Epoch 18/50\n",
      "23368/23368 [==============================] - 3s 110us/step - loss: 0.0030 - val_loss: 0.0032\n",
      "Epoch 19/50\n",
      "23368/23368 [==============================] - 2s 95us/step - loss: 0.0030 - val_loss: 0.0034\n",
      "Epoch 20/50\n",
      "23368/23368 [==============================] - 2s 93us/step - loss: 0.0030 - val_loss: 0.0026\n",
      "Epoch 21/50\n",
      "23368/23368 [==============================] - 2s 105us/step - loss: 0.0030 - val_loss: 0.0026\n",
      "Epoch 22/50\n",
      "23368/23368 [==============================] - 3s 114us/step - loss: 0.0030 - val_loss: 0.0025\n",
      "Epoch 23/50\n",
      "23368/23368 [==============================] - 4s 150us/step - loss: 0.0030 - val_loss: 0.0025\n",
      "Epoch 24/50\n",
      "23368/23368 [==============================] - 2s 97us/step - loss: 0.0029 - val_loss: 0.0026\n",
      "Epoch 25/50\n",
      "23368/23368 [==============================] - 2s 89us/step - loss: 0.0029 - val_loss: 0.0027\n",
      "Epoch 26/50\n",
      "23368/23368 [==============================] - 2s 96us/step - loss: 0.0029 - val_loss: 0.0028\n",
      "Epoch 27/50\n",
      "23368/23368 [==============================] - 2s 87us/step - loss: 0.0029 - val_loss: 0.0027\n",
      "Epoch 28/50\n",
      "23368/23368 [==============================] - 2s 101us/step - loss: 0.0029 - val_loss: 0.0025\n",
      "Epoch 29/50\n",
      "23368/23368 [==============================] - 2s 95us/step - loss: 0.0029 - val_loss: 0.0025\n",
      "Epoch 30/50\n",
      "23368/23368 [==============================] - 2s 87us/step - loss: 0.0029 - val_loss: 0.0024\n",
      "Epoch 31/50\n",
      "23368/23368 [==============================] - 2s 100us/step - loss: 0.0029 - val_loss: 0.0025\n",
      "Epoch 32/50\n",
      "23368/23368 [==============================] - 3s 113us/step - loss: 0.0029 - val_loss: 0.0025\n",
      "Epoch 33/50\n",
      "23368/23368 [==============================] - 3s 117us/step - loss: 0.0029 - val_loss: 0.0024\n",
      "Epoch 34/50\n",
      "23368/23368 [==============================] - 2s 95us/step - loss: 0.0028 - val_loss: 0.0024\n",
      "Epoch 35/50\n",
      "23368/23368 [==============================] - 3s 107us/step - loss: 0.0028 - val_loss: 0.0026\n",
      "Epoch 36/50\n",
      "23368/23368 [==============================] - 2s 99us/step - loss: 0.0028 - val_loss: 0.0028\n",
      "Epoch 37/50\n",
      "23368/23368 [==============================] - 3s 118us/step - loss: 0.0028 - val_loss: 0.0028\n",
      "Epoch 38/50\n",
      "23368/23368 [==============================] - 3s 113us/step - loss: 0.0028 - val_loss: 0.0023\n",
      "Epoch 39/50\n",
      "23368/23368 [==============================] - 2s 99us/step - loss: 0.0028 - val_loss: 0.0023\n",
      "Epoch 40/50\n",
      "23368/23368 [==============================] - 2s 95us/step - loss: 0.0028 - val_loss: 0.0023\n",
      "Epoch 41/50\n",
      "23368/23368 [==============================] - 2s 104us/step - loss: 0.0028 - val_loss: 0.0024\n",
      "Epoch 42/50\n",
      "23368/23368 [==============================] - 2s 98us/step - loss: 0.0028 - val_loss: 0.0023\n",
      "Epoch 43/50\n",
      "23368/23368 [==============================] - 2s 96us/step - loss: 0.0028 - val_loss: 0.0024\n",
      "Epoch 44/50\n",
      "23368/23368 [==============================] - 2s 91us/step - loss: 0.0027 - val_loss: 0.0023\n",
      "Epoch 45/50\n",
      "23368/23368 [==============================] - 2s 103us/step - loss: 0.0027 - val_loss: 0.0023\n",
      "Epoch 46/50\n",
      "23368/23368 [==============================] - 2s 93us/step - loss: 0.0027 - val_loss: 0.0023\n",
      "Epoch 47/50\n",
      "23368/23368 [==============================] - 2s 100us/step - loss: 0.0027 - val_loss: 0.0023\n",
      "Epoch 48/50\n",
      "23368/23368 [==============================] - 4s 150us/step - loss: 0.0027 - val_loss: 0.0022\n",
      "Epoch 49/50\n",
      "23368/23368 [==============================] - 3s 111us/step - loss: 0.0027 - val_loss: 0.0029\n",
      "Epoch 50/50\n",
      "23368/23368 [==============================] - 2s 99us/step - loss: 0.0027 - val_loss: 0.0023\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x149a0b1cef0>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(train_inputs['X'],\n",
    "          train_inputs['target'],\n",
    "          batch_size=BATCH_SIZE,\n",
    "          epochs=EPOCHS,\n",
    "          validation_data=(valid_inputs['X'], valid_inputs['target']),\n",
    "          callbacks=[earlystop],\n",
    "          verbose=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Evaluate the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "look_back_dt = dt.datetime.strptime(test_start_dt, '%Y-%m-%d %H:%M:%S') - dt.timedelta(hours=T-1)\n",
    "test = energy.copy()[test_start_dt:][['load', 'temp']]\n",
    "test[['load', 'temp']] = X_scaler.transform(test)\n",
    "test_inputs = TimeSeriesTensor(test, 'load', HORIZON, tensor_structure)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "predictions = model.predict(test_inputs['X'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.21, 0.28, 0.34],\n",
       "       [0.29, 0.35, 0.4 ],\n",
       "       [0.36, 0.4 , 0.43],\n",
       "       ...,\n",
       "       [0.61, 0.54, 0.46],\n",
       "       [0.56, 0.5 , 0.44],\n",
       "       [0.52, 0.48, 0.43]], dtype=float32)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>timestamp</th>\n",
       "      <th>h</th>\n",
       "      <th>prediction</th>\n",
       "      <th>actual</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2014-11-01 05:00:00</td>\n",
       "      <td>t+1</td>\n",
       "      <td>2,669.07</td>\n",
       "      <td>2,714.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2014-11-01 06:00:00</td>\n",
       "      <td>t+1</td>\n",
       "      <td>2,922.95</td>\n",
       "      <td>2,970.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2014-11-01 07:00:00</td>\n",
       "      <td>t+1</td>\n",
       "      <td>3,156.33</td>\n",
       "      <td>3,189.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2014-11-01 08:00:00</td>\n",
       "      <td>t+1</td>\n",
       "      <td>3,292.53</td>\n",
       "      <td>3,356.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2014-11-01 09:00:00</td>\n",
       "      <td>t+1</td>\n",
       "      <td>3,446.14</td>\n",
       "      <td>3,436.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            timestamp    h  prediction   actual\n",
       "0 2014-11-01 05:00:00  t+1    2,669.07 2,714.00\n",
       "1 2014-11-01 06:00:00  t+1    2,922.95 2,970.00\n",
       "2 2014-11-01 07:00:00  t+1    3,156.33 3,189.00\n",
       "3 2014-11-01 08:00:00  t+1    3,292.53 3,356.00\n",
       "4 2014-11-01 09:00:00  t+1    3,446.14 3,436.00"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "eval_df = create_evaluation_df(predictions, test_inputs, HORIZON, y_scaler)\n",
    "eval_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Compute MAPE for each forecast horizon"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "h\n",
       "t+1   0.02\n",
       "t+2   0.04\n",
       "t+3   0.06\n",
       "Name: APE, dtype: float64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "eval_df['APE'] = (eval_df['prediction'] - eval_df['actual']).abs() / eval_df['actual']\n",
    "eval_df.groupby('h')['APE'].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Compute MAPE across all predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.03622875962705554"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mape(eval_df['prediction'], eval_df['actual'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot actuals vs predictions at each horizon for first week of the test period. As is to be expected, predictions for one step ahead (*t+1*) are more accurate than those for 2 or 3 steps ahead"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_df = eval_df[(eval_df.timestamp<'2014-11-08') & (eval_df.h=='t+1')][['timestamp', 'actual']]\n",
    "for t in range(1, HORIZON+1):\n",
    "    plot_df['t+'+str(t)] = eval_df[(eval_df.timestamp<'2014-11-08') & (eval_df.h=='t+'+str(t))]['prediction'].values\n",
    "\n",
    "fig = plt.figure(figsize=(15, 8))\n",
    "ax = plt.plot(plot_df['timestamp'], plot_df['actual'], color='red', linewidth=4.0)\n",
    "ax = fig.add_subplot(111)\n",
    "ax.plot(plot_df['timestamp'], plot_df['t+1'], color='blue', linewidth=4.0, alpha=0.75)\n",
    "ax.plot(plot_df['timestamp'], plot_df['t+2'], color='blue', linewidth=3.0, alpha=0.5)\n",
    "ax.plot(plot_df['timestamp'], plot_df['t+3'], color='blue', linewidth=2.0, alpha=0.25)\n",
    "plt.xlabel('timestamp', fontsize=12)\n",
    "plt.ylabel('load', fontsize=12)\n",
    "ax.legend(loc='best')\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.5",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
